Data extraction services for records that were never meant to be a dataset.
Public records are published to be read by people: PDFs, scanned instruments, inconsistent tables, one format per county. Recordpipe turns them into data: automated extraction, OCR where records are images, field-level normalization across jurisdictions, and delivery in the schema your team defined. One pipeline, one output, however many sources feed it.
Automated data capture
Structured and semi-structured records collected and parsed on cadence.
Document and PDF data extraction
Recorded instruments and filings processed with OCR and field extraction, with source references per row.
Data aggregation across jurisdictions
Fifty formats normalized to one schema; entity and address resolution applied.
Quality you can audit
Acceptance criteria in scoping; every delivered row traceable to its public source.
Data extraction services built for public records at production scale
Data extraction services from Recordpipe take the public record as it exists — search results, index pages, filing detail screens, scanned instruments, downloadable PDFs — and return one normalized dataset your systems can use. County recorders, assessors, courts, state registries, licensing boards, and permit offices each publish differently. Automated data extraction is the discipline of reading every one of those formats and writing the same schema out the other side.
We are a data extraction company that runs the extraction, not a tool you configure. Our RPA and automation bots collect the source material, parsers and document models pull the fields, and validation rules check every batch against the prior one before it ships. The infrastructure behind it processes over one million public records nightly.
What you receive is data, not pages: typed fields, resolved entities, a capture date and source category on every row. Public data only — nothing login-walled, nothing private, no breached data, under any contract. The engagement is fixed-price and the output is yours to load, query, and build on.
Document data extraction: PDF, OCR, and recorded instruments into one schema
A large share of the public record is not a table. It is a scanned deed, a stamped lien, a court order in PDF, a permit application filled in by hand. Document data extraction is where most in-house projects stall, and it is a core part of what we deliver. Our OCR data extraction pipeline reads the image, recovers the text, and then applies field models built for the instrument type: grantor and grantee on a deed, secured party and collateral on a financing statement, case number and disposition on a judgment.
PDF data extraction services are only as good as their validation. Every extracted field carries a confidence score. Low-confidence values route to a review queue before delivery, and cross-checks against the index record — the party names and document number the office already typed — catch OCR errors a model alone would miss.
The result is the recorded instrument as structured rows, not a folder of images. Original page images can ship alongside the structured data, linked by document identifier, for teams whose workflow still needs to see the source.
Automated data capture on a cadence, not a one-time data mining project
A data mining service that hands you a file and leaves is a project. Automated data capture that runs every night and tells you what changed is infrastructure. Recordpipe builds for the second. After the initial backfill, the pipeline runs on the cadence set in scoping — daily or weekly — and each run delivers the full snapshot plus a change delta: new filings, updated records, and entries no longer present at source.
Aggregation is the other half. A data aggregation service earns its keep when the same fact is published differently across jurisdictions. Our pipeline maps every source's fields to one schema, resolves entities that appear under variant spellings or formats, and deduplicates across sources, so a lien recorded in one county and a judgment entered in another attach to the same party in your data.
The AI analysis layer runs on top: filtering to your criteria, ranking by the signals you care about, and flagging records that merit attention. The data collection company you hire should make the data smaller and sharper on the way in, not just bigger.
How a data extraction company scopes, prices, and delivers the work
The engagement starts with a free intake form: which record types, which jurisdictions, what schema you need, and how the data should arrive. We reply within 2 business days with feasibility and an approach. The $500 scoping, credited in full against the contract, produces a real sample — extracted rows from your actual sources, including document fields if scanned instruments are in scope — and a fixed quote within 5 business days.
Contracts are fixed-price, from $5,000 to $3 million, sized by the number and variety of sources, the document extraction load, and the cadence. Ongoing collection, maintenance, and monitoring run under a flat retainer quoted with the build. When a source changes its layout or process, we repair the pipeline; the retainer covers it and your team does not file a ticket.
Delivery matches your stack: files to S3, GCS, or SFTP; tables loaded into your warehouse; webhooks for records that match a rule; or an API. The output schema is versioned, documented, and agreed before the first production run, so your engineers integrate once.
What an engagement delivers
| Deliverable | Description |
|---|---|
| Source inventory | Every source in scope catalogued by record type, jurisdiction, format, and update pattern — the map the pipeline is built against. |
| Extraction pipeline | Automated collection and parsing for each source, operated and maintained by Recordpipe on your cadence. |
| Document capture | OCR and field models for scanned instruments and PDFs, with a confidence score per extracted field and a review queue for low-confidence values. |
| Normalized schema | One agreed field set across all sources, with types, allowed values, and a data dictionary your engineers can build against. |
| Entity resolution | Parties, parcels, and cases matched across sources and variant spellings, with a stable identifier your systems can key on. |
| Validation report | Per-run checks: row counts against the prior run, field completeness, cross-checks between document text and index data. |
| Provenance columns | Capture date, source category, and document identifier on every row, so any value can be traced to where it came from. |
| Change delta | Rows added, updated, or no longer present at source since the last run, delivered alongside the full snapshot. |
| Delivery | Files, warehouse tables, webhooks, or API — configured to your stack and tested before launch. |
| Source maintenance | Repairs when a source changes, covered by the retainer, with monitoring that catches drift before you do. |
In the field.
Instrument indexing at a title company
A title company's operations team can receive recorded deeds, mortgages, and liens for its counties as structured rows — grantor, grantee, legal description, recording date — extracted from the scanned instruments and cross-checked against the index. Examiners start from typed data with the page image linked, instead of reading images by hand.
Cross-state lien aggregation at a commercial lender
A lender's credit team can have financing statements and tax liens across every state it lends in aggregated into one schema, with debtor names resolved across spelling variants. A weekly delta flags new filings against existing borrowers, delivered to the warehouse the portfolio team already queries.
Permit and inspection capture for a construction analytics product
A construction analytics team can capture permit applications and inspection results from municipal offices that publish as PDFs and web forms alike, normalized into one permit record with contractor, valuation, and status fields. The product ships on the data; the vendor runs the extraction.
Judgment and docket extraction at a legal research firm
A legal research firm can commission extraction of civil judgments and docket entries in its covered courts, with case number, parties, disposition, and amount pulled from orders that exist only as PDFs. Analysts query structured cases; nobody on the team opens a court document to transcribe it.
Data extraction company, data collection company, data mining service: what we are and are not
We extract from publicly available records only. No login-walled sites, no private data, no personal data harvesting. Fixed-price from $5,000; scoping is $500, credited, with a sample in 5 business days.